Overview

Dataset statistics

Number of variables18
Number of observations3116
Missing cells0
Missing cells (%)0.0%
Duplicate rows19
Duplicate rows (%)0.6%
Total size in memory462.5 KiB
Average record size in memory152.0 B

Variable types

Numeric8
Categorical10

Alerts

Dataset has 19 (0.6%) duplicate rowsDuplicates
emp.var.rate is highly overall correlated with cons.price.idx and 3 other fieldsHigh correlation
cons.price.idx is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
cons.conf.idx is highly overall correlated with monthHigh correlation
euribor3m is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
nr.employed is highly overall correlated with emp.var.rate and 2 other fieldsHigh correlation
contact is highly overall correlated with cons.price.idx and 1 other fieldsHigh correlation
month is highly overall correlated with emp.var.rate and 5 other fieldsHigh correlation
default is highly imbalanced (99.6%)Imbalance
previous has 2622 (84.1%) zerosZeros

Reproduction

Analysis started2023-11-04 03:30:33.080677
Analysis finished2023-11-04 03:30:37.410284
Duration4.33 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

age
Real number (ℝ)

Distinct62
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.236521
Minimum20
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.7 KiB
2023-11-03T20:30:37.462188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile26
Q131
median37
Q346
95-th percentile58
Maximum88
Range68
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.225997
Coefficient of variation (CV)0.26062445
Kurtosis0.7335795
Mean39.236521
Median Absolute Deviation (MAD)7
Skewness0.87016679
Sum122261
Variance104.57101
MonotonicityNot monotonic
2023-11-03T20:30:37.530669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 189
 
6.1%
31 166
 
5.3%
34 148
 
4.7%
30 147
 
4.7%
33 138
 
4.4%
35 135
 
4.3%
36 125
 
4.0%
29 123
 
3.9%
38 114
 
3.7%
41 106
 
3.4%
Other values (52) 1725
55.4%
ValueCountFrequency (%)
20 2
 
0.1%
21 7
 
0.2%
22 9
 
0.3%
23 15
 
0.5%
24 47
 
1.5%
25 48
 
1.5%
26 54
1.7%
27 74
2.4%
28 89
2.9%
29 123
3.9%
ValueCountFrequency (%)
88 1
 
< 0.1%
82 2
0.1%
81 3
0.1%
80 4
0.1%
78 3
0.1%
77 1
 
< 0.1%
76 3
0.1%
75 2
0.1%
74 3
0.1%
73 3
0.1%

job
Categorical

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
admin.
857 
technician
574 
blue-collar
555 
services
277 
management
265 
Other values (7)
588 

Length

Max length13
Median length12
Mean length8.8822208
Min length6

Characters and Unicode

Total characters27677
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblue-collar
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin. 857
27.5%
technician 574
18.4%
blue-collar 555
17.8%
services 277
 
8.9%
management 265
 
8.5%
self-employed 127
 
4.1%
retired 120
 
3.9%
entrepreneur 107
 
3.4%
unemployed 87
 
2.8%
housemaid 75
 
2.4%
Other values (2) 72
 
2.3%

Length

2023-11-03T20:30:37.595493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin 857
27.5%
technician 574
18.4%
blue-collar 555
17.8%
services 277
 
8.9%
management 265
 
8.5%
self-employed 127
 
4.1%
retired 120
 
3.9%
entrepreneur 107
 
3.4%
unemployed 87
 
2.8%
housemaid 75
 
2.4%
Other values (2) 72
 
2.3%

Most occurring characters

ValueCountFrequency (%)
e 3566
12.9%
n 2942
10.6%
a 2591
 
9.4%
i 2477
 
8.9%
l 2006
 
7.2%
c 1980
 
7.2%
m 1676
 
6.1%
r 1393
 
5.0%
d 1321
 
4.8%
t 1176
 
4.2%
Other values (14) 6549
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 26138
94.4%
Other Punctuation 857
 
3.1%
Dash Punctuation 682
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3566
13.6%
n 2942
11.3%
a 2591
9.9%
i 2477
9.5%
l 2006
7.7%
c 1980
 
7.6%
m 1676
 
6.4%
r 1393
 
5.3%
d 1321
 
5.1%
t 1176
 
4.5%
Other values (12) 5010
19.2%
Other Punctuation
ValueCountFrequency (%)
. 857
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 682
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26138
94.4%
Common 1539
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 3566
13.6%
n 2942
11.3%
a 2591
9.9%
i 2477
9.5%
l 2006
7.7%
c 1980
 
7.6%
m 1676
 
6.4%
r 1393
 
5.3%
d 1321
 
5.1%
t 1176
 
4.5%
Other values (12) 5010
19.2%
Common
ValueCountFrequency (%)
. 857
55.7%
- 682
44.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27677
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 3566
12.9%
n 2942
10.6%
a 2591
 
9.4%
i 2477
 
8.9%
l 2006
 
7.2%
c 1980
 
7.2%
m 1676
 
6.1%
r 1393
 
5.0%
d 1321
 
4.8%
t 1176
 
4.2%
Other values (14) 6549
23.7%

marital
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
married
1803 
single
956 
divorced
347 
unknown
 
10

Length

Max length8
Median length7
Mean length6.8045571
Min length6

Characters and Unicode

Total characters21203
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowsingle
3rd rowmarried
4th rowmarried
5th rowsingle

Common Values

ValueCountFrequency (%)
married 1803
57.9%
single 956
30.7%
divorced 347
 
11.1%
unknown 10
 
0.3%

Length

2023-11-03T20:30:37.655581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:37.711376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
married 1803
57.9%
single 956
30.7%
divorced 347
 
11.1%
unknown 10
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r 3953
18.6%
i 3106
14.6%
e 3106
14.6%
d 2497
11.8%
m 1803
8.5%
a 1803
8.5%
n 986
 
4.7%
s 956
 
4.5%
g 956
 
4.5%
l 956
 
4.5%
Other values (6) 1081
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 21203
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 3953
18.6%
i 3106
14.6%
e 3106
14.6%
d 2497
11.8%
m 1803
8.5%
a 1803
8.5%
n 986
 
4.7%
s 956
 
4.5%
g 956
 
4.5%
l 956
 
4.5%
Other values (6) 1081
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 21203
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 3953
18.6%
i 3106
14.6%
e 3106
14.6%
d 2497
11.8%
m 1803
8.5%
a 1803
8.5%
n 986
 
4.7%
s 956
 
4.5%
g 956
 
4.5%
l 956
 
4.5%
Other values (6) 1081
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 3953
18.6%
i 3106
14.6%
e 3106
14.6%
d 2497
11.8%
m 1803
8.5%
a 1803
8.5%
n 986
 
4.7%
s 956
 
4.5%
g 956
 
4.5%
l 956
 
4.5%
Other values (6) 1081
 
5.1%

education
Categorical

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
university.degree
1114 
high.school
732 
professional.course
456 
basic.9y
412 
basic.4y
251 

Length

Max length19
Median length17
Mean length13.532092
Min length8

Characters and Unicode

Total characters42166
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.9y
2nd rowhigh.school
3rd rowhigh.school
4th rowuniversity.degree
5th rowuniversity.degree

Common Values

ValueCountFrequency (%)
university.degree 1114
35.8%
high.school 732
23.5%
professional.course 456
14.6%
basic.9y 412
 
13.2%
basic.4y 251
 
8.1%
basic.6y 151
 
4.8%

Length

2023-11-03T20:30:37.766676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:37.817180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree 1114
35.8%
high.school 732
23.5%
professional.course 456
14.6%
basic.9y 412
 
13.2%
basic.4y 251
 
8.1%
basic.6y 151
 
4.8%

Most occurring characters

ValueCountFrequency (%)
e 5368
12.7%
i 4230
 
10.0%
s 4028
 
9.6%
r 3140
 
7.4%
. 3116
 
7.4%
o 2832
 
6.7%
h 2196
 
5.2%
c 2002
 
4.7%
y 1928
 
4.6%
g 1846
 
4.4%
Other values (13) 11480
27.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 38236
90.7%
Other Punctuation 3116
 
7.4%
Decimal Number 814
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5368
14.0%
i 4230
11.1%
s 4028
10.5%
r 3140
 
8.2%
o 2832
 
7.4%
h 2196
 
5.7%
c 2002
 
5.2%
y 1928
 
5.0%
g 1846
 
4.8%
n 1570
 
4.1%
Other values (9) 9096
23.8%
Decimal Number
ValueCountFrequency (%)
9 412
50.6%
4 251
30.8%
6 151
 
18.6%
Other Punctuation
ValueCountFrequency (%)
. 3116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 38236
90.7%
Common 3930
 
9.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5368
14.0%
i 4230
11.1%
s 4028
10.5%
r 3140
 
8.2%
o 2832
 
7.4%
h 2196
 
5.7%
c 2002
 
5.2%
y 1928
 
5.0%
g 1846
 
4.8%
n 1570
 
4.1%
Other values (9) 9096
23.8%
Common
ValueCountFrequency (%)
. 3116
79.3%
9 412
 
10.5%
4 251
 
6.4%
6 151
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42166
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5368
12.7%
i 4230
 
10.0%
s 4028
 
9.6%
r 3140
 
7.4%
. 3116
 
7.4%
o 2832
 
6.7%
h 2196
 
5.2%
c 2002
 
4.7%
y 1928
 
4.6%
g 1846
 
4.4%
Other values (13) 11480
27.2%

default
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
0
3115 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3116
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3115
> 99.9%
1 1
 
< 0.1%

Length

2023-11-03T20:30:37.875446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:37.917829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3115
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 3115
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3116
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3115
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3115
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3115
> 99.9%
1 1
 
< 0.1%

housing
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
1
1701 
0
1415 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3116
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 1701
54.6%
0 1415
45.4%

Length

2023-11-03T20:30:37.964039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:38.010427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1701
54.6%
0 1415
45.4%

Most occurring characters

ValueCountFrequency (%)
1 1701
54.6%
0 1415
45.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3116
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1701
54.6%
0 1415
45.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1701
54.6%
0 1415
45.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1701
54.6%
0 1415
45.4%

loan
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
0
2602 
1
514 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3116
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2602
83.5%
1 514
 
16.5%

Length

2023-11-03T20:30:38.054657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:38.099189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2602
83.5%
1 514
 
16.5%

Most occurring characters

ValueCountFrequency (%)
0 2602
83.5%
1 514
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3116
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2602
83.5%
1 514
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2602
83.5%
1 514
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2602
83.5%
1 514
 
16.5%

contact
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
cellular
2127 
telephone
989 

Length

Max length9
Median length8
Mean length8.3173941
Min length8

Characters and Unicode

Total characters25917
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcellular
2nd rowtelephone
3rd rowtelephone
4th rowcellular
5th rowcellular

Common Values

ValueCountFrequency (%)
cellular 2127
68.3%
telephone 989
31.7%

Length

2023-11-03T20:30:38.144500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:38.187813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular 2127
68.3%
telephone 989
31.7%

Most occurring characters

ValueCountFrequency (%)
l 7370
28.4%
e 5094
19.7%
c 2127
 
8.2%
u 2127
 
8.2%
a 2127
 
8.2%
r 2127
 
8.2%
t 989
 
3.8%
p 989
 
3.8%
h 989
 
3.8%
o 989
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 25917
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 7370
28.4%
e 5094
19.7%
c 2127
 
8.2%
u 2127
 
8.2%
a 2127
 
8.2%
r 2127
 
8.2%
t 989
 
3.8%
p 989
 
3.8%
h 989
 
3.8%
o 989
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 25917
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 7370
28.4%
e 5094
19.7%
c 2127
 
8.2%
u 2127
 
8.2%
a 2127
 
8.2%
r 2127
 
8.2%
t 989
 
3.8%
p 989
 
3.8%
h 989
 
3.8%
o 989
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25917
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 7370
28.4%
e 5094
19.7%
c 2127
 
8.2%
u 2127
 
8.2%
a 2127
 
8.2%
r 2127
 
8.2%
t 989
 
3.8%
p 989
 
3.8%
h 989
 
3.8%
o 989
 
3.8%

month
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
may
992 
jul
519 
aug
497 
nov
389 
jun
369 
Other values (5)
350 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9348
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowjun
4th rownov
5th rowsep

Common Values

ValueCountFrequency (%)
may 992
31.8%
jul 519
16.7%
aug 497
15.9%
nov 389
 
12.5%
jun 369
 
11.8%
apr 170
 
5.5%
oct 61
 
2.0%
sep 57
 
1.8%
mar 42
 
1.3%
dec 20
 
0.6%

Length

2023-11-03T20:30:38.233938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:38.287005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
may 992
31.8%
jul 519
16.7%
aug 497
15.9%
nov 389
 
12.5%
jun 369
 
11.8%
apr 170
 
5.5%
oct 61
 
2.0%
sep 57
 
1.8%
mar 42
 
1.3%
dec 20
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 1701
18.2%
u 1385
14.8%
m 1034
11.1%
y 992
10.6%
j 888
9.5%
n 758
8.1%
l 519
 
5.6%
g 497
 
5.3%
o 450
 
4.8%
v 389
 
4.2%
Other values (7) 735
7.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9348
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1701
18.2%
u 1385
14.8%
m 1034
11.1%
y 992
10.6%
j 888
9.5%
n 758
8.1%
l 519
 
5.6%
g 497
 
5.3%
o 450
 
4.8%
v 389
 
4.2%
Other values (7) 735
7.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 9348
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1701
18.2%
u 1385
14.8%
m 1034
11.1%
y 992
10.6%
j 888
9.5%
n 758
8.1%
l 519
 
5.6%
g 497
 
5.3%
o 450
 
4.8%
v 389
 
4.2%
Other values (7) 735
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1701
18.2%
u 1385
14.8%
m 1034
11.1%
y 992
10.6%
j 888
9.5%
n 758
8.1%
l 519
 
5.6%
g 497
 
5.3%
o 450
 
4.8%
v 389
 
4.2%
Other values (7) 735
7.9%

day_of_week
Categorical

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
mon
646 
thu
641 
wed
628 
tue
619 
fri
582 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9348
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfri
2nd rowfri
3rd rowwed
4th rowmon
5th rowthu

Common Values

ValueCountFrequency (%)
mon 646
20.7%
thu 641
20.6%
wed 628
20.2%
tue 619
19.9%
fri 582
18.7%

Length

2023-11-03T20:30:38.348312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:38.395039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
mon 646
20.7%
thu 641
20.6%
wed 628
20.2%
tue 619
19.9%
fri 582
18.7%

Most occurring characters

ValueCountFrequency (%)
t 1260
13.5%
u 1260
13.5%
e 1247
13.3%
m 646
6.9%
o 646
6.9%
n 646
6.9%
h 641
6.9%
w 628
6.7%
d 628
6.7%
f 582
6.2%
Other values (2) 1164
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9348
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1260
13.5%
u 1260
13.5%
e 1247
13.3%
m 646
6.9%
o 646
6.9%
n 646
6.9%
h 641
6.9%
w 628
6.7%
d 628
6.7%
f 582
6.2%
Other values (2) 1164
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 9348
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1260
13.5%
u 1260
13.5%
e 1247
13.3%
m 646
6.9%
o 646
6.9%
n 646
6.9%
h 641
6.9%
w 628
6.7%
d 628
6.7%
f 582
6.2%
Other values (2) 1164
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1260
13.5%
u 1260
13.5%
e 1247
13.3%
m 646
6.9%
o 646
6.9%
n 646
6.9%
h 641
6.9%
w 628
6.7%
d 628
6.7%
f 582
6.2%
Other values (2) 1164
12.5%

campaign
Real number (ℝ)

Distinct25
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5057766
Minimum1
Maximum35
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.7 KiB
2023-11-03T20:30:38.445044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum35
Range34
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.595149
Coefficient of variation (CV)1.0356665
Kurtosis29.061924
Mean2.5057766
Median Absolute Deviation (MAD)1
Skewness4.2942059
Sum7808
Variance6.7347981
MonotonicityNot monotonic
2023-11-03T20:30:38.497655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 1359
43.6%
2 804
25.8%
3 387
 
12.4%
4 211
 
6.8%
5 107
 
3.4%
6 79
 
2.5%
7 42
 
1.3%
8 27
 
0.9%
9 22
 
0.7%
11 15
 
0.5%
Other values (15) 63
 
2.0%
ValueCountFrequency (%)
1 1359
43.6%
2 804
25.8%
3 387
 
12.4%
4 211
 
6.8%
5 107
 
3.4%
6 79
 
2.5%
7 42
 
1.3%
8 27
 
0.9%
9 22
 
0.7%
10 12
 
0.4%
ValueCountFrequency (%)
35 1
 
< 0.1%
29 2
 
0.1%
27 1
 
< 0.1%
24 1
 
< 0.1%
23 2
 
0.1%
22 2
 
0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 10
0.3%
16 3
 
0.1%

previous
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20892169
Minimum0
Maximum6
Zeros2622
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size48.7 KiB
2023-11-03T20:30:38.549157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.56060961
Coefficient of variation (CV)2.683348
Kurtosis18.334341
Mean0.20892169
Median Absolute Deviation (MAD)0
Skewness3.6919975
Sum651
Variance0.31428314
MonotonicityNot monotonic
2023-11-03T20:30:38.599793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2622
84.1%
1 389
 
12.5%
2 71
 
2.3%
3 20
 
0.6%
4 11
 
0.4%
5 2
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 2622
84.1%
1 389
 
12.5%
2 71
 
2.3%
3 20
 
0.6%
4 11
 
0.4%
5 2
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 2
 
0.1%
4 11
 
0.4%
3 20
 
0.6%
2 71
 
2.3%
1 389
 
12.5%
0 2622
84.1%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.048106547
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative1460
Negative (%)46.9%
Memory size48.7 KiB
2023-11-03T20:30:38.651516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.5982312
Coefficient of variation (CV)-33.222739
Kurtosis-1.2235742
Mean-0.048106547
Median Absolute Deviation (MAD)0.3
Skewness-0.57534557
Sum-149.9
Variance2.5543431
MonotonicityNot monotonic
2023-11-03T20:30:38.700380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.4 1169
37.5%
-1.8 717
23.0%
1.1 487
15.6%
-0.1 337
 
10.8%
-2.9 140
 
4.5%
-3.4 95
 
3.0%
-1.7 76
 
2.4%
-1.1 75
 
2.4%
-3 19
 
0.6%
-0.2 1
 
< 0.1%
ValueCountFrequency (%)
-3.4 95
 
3.0%
-3 19
 
0.6%
-2.9 140
 
4.5%
-1.8 717
23.0%
-1.7 76
 
2.4%
-1.1 75
 
2.4%
-0.2 1
 
< 0.1%
-0.1 337
 
10.8%
1.1 487
15.6%
1.4 1169
37.5%
ValueCountFrequency (%)
1.4 1169
37.5%
1.1 487
15.6%
-0.1 337
 
10.8%
-0.2 1
 
< 0.1%
-1.1 75
 
2.4%
-1.7 76
 
2.4%
-1.8 717
23.0%
-2.9 140
 
4.5%
-3 19
 
0.6%
-3.4 95
 
3.0%

cons.price.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.531871
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.7 KiB
2023-11-03T20:30:38.757174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.649
Q193.075
median93.444
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.58486158
Coefficient of variation (CV)0.0062530726
Kurtosis-0.85156193
Mean93.531871
Median Absolute Deviation (MAD)0.55
Skewness-0.094865709
Sum291445.31
Variance0.34206306
MonotonicityNot monotonic
2023-11-03T20:30:38.822222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.994 487
15.6%
92.893 486
15.6%
93.918 480
15.4%
93.444 408
13.1%
93.2 332
10.7%
94.465 281
9.0%
93.075 160
 
5.1%
92.963 66
 
2.1%
92.201 62
 
2.0%
92.431 38
 
1.2%
Other values (16) 316
10.1%
ValueCountFrequency (%)
92.201 62
 
2.0%
92.379 24
 
0.8%
92.431 38
 
1.2%
92.469 12
 
0.4%
92.649 33
 
1.1%
92.713 19
 
0.6%
92.756 1
 
< 0.1%
92.843 24
 
0.8%
92.893 486
15.6%
92.963 66
 
2.1%
ValueCountFrequency (%)
94.767 24
 
0.8%
94.601 18
 
0.6%
94.465 281
9.0%
94.215 27
 
0.9%
94.199 33
 
1.1%
94.055 22
 
0.7%
94.027 27
 
0.9%
93.994 487
15.6%
93.918 480
15.4%
93.876 19
 
0.6%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.61518
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative3116
Negative (%)100.0%
Memory size48.7 KiB
2023-11-03T20:30:38.880861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-31.4
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.742187
Coefficient of variation (CV)-0.11675898
Kurtosis-0.26723084
Mean-40.61518
Median Absolute Deviation (MAD)4.4
Skewness0.35230149
Sum-126556.9
Variance22.488338
MonotonicityNot monotonic
2023-11-03T20:30:38.933756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.4 487
15.6%
-46.2 486
15.6%
-42.7 480
15.4%
-36.1 408
13.1%
-42 332
10.7%
-41.8 281
9.0%
-47.1 160
 
5.1%
-40.8 66
 
2.1%
-31.4 62
 
2.0%
-26.9 38
 
1.2%
Other values (16) 316
10.1%
ValueCountFrequency (%)
-50.8 24
 
0.8%
-50 24
 
0.8%
-49.5 18
 
0.6%
-47.1 160
 
5.1%
-46.2 486
15.6%
-45.9 1
 
< 0.1%
-42.7 480
15.4%
-42 332
10.7%
-41.8 281
9.0%
-40.8 66
 
2.1%
ValueCountFrequency (%)
-26.9 38
 
1.2%
-29.8 24
 
0.8%
-30.1 33
 
1.1%
-31.4 62
 
2.0%
-33 19
 
0.6%
-33.6 12
 
0.4%
-34.6 10
 
0.3%
-34.8 18
 
0.6%
-36.1 408
13.1%
-36.4 487
15.6%

euribor3m
Real number (ℝ)

HIGH CORRELATION 

Distinct226
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4815629
Minimum0.635
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.7 KiB
2023-11-03T20:30:38.994097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.635
5-th percentile0.75125
Q11.313
median4.856
Q34.961
95-th percentile4.966
Maximum5.045
Range4.41
Interquartile range (IQR)3.648

Descriptive statistics

Standard deviation1.7700993
Coefficient of variation (CV)0.5084209
Kurtosis-1.5987711
Mean3.4815629
Median Absolute Deviation (MAD)0.111
Skewness-0.55399565
Sum10848.55
Variance3.1332517
MonotonicityNot monotonic
2023-11-03T20:30:39.064196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.963 192
 
6.2%
4.962 179
 
5.7%
4.857 173
 
5.6%
4.961 132
 
4.2%
4.965 93
 
3.0%
4.964 93
 
3.0%
4.076 83
 
2.7%
4.856 80
 
2.6%
1.405 77
 
2.5%
4.96 75
 
2.4%
Other values (216) 1939
62.2%
ValueCountFrequency (%)
0.635 3
0.1%
0.639 2
 
0.1%
0.644 5
0.2%
0.645 2
 
0.1%
0.646 3
0.1%
0.649 2
 
0.1%
0.65 1
 
< 0.1%
0.652 4
0.1%
0.654 1
 
< 0.1%
0.655 2
 
0.1%
ValueCountFrequency (%)
5.045 1
 
< 0.1%
4.97 14
 
0.4%
4.968 67
 
2.2%
4.967 42
 
1.3%
4.966 54
 
1.7%
4.965 93
3.0%
4.964 93
3.0%
4.963 192
6.2%
4.962 179
5.7%
4.961 132
4.2%

nr.employed
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5161.1172
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.7 KiB
2023-11-03T20:30:39.116442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation76.422717
Coefficient of variation (CV)0.014807398
Kurtosis-0.25792718
Mean5161.1172
Median Absolute Deviation (MAD)37.1
Skewness-0.94472331
Sum16082041
Variance5840.4317
MonotonicityNot monotonic
2023-11-03T20:30:39.166774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.1 1169
37.5%
5099.1 670
21.5%
5191 487
15.6%
5195.8 337
 
10.8%
5076.2 140
 
4.5%
5017.5 95
 
3.0%
4991.6 76
 
2.4%
4963.6 75
 
2.4%
5008.7 47
 
1.5%
5023.5 19
 
0.6%
ValueCountFrequency (%)
4963.6 75
 
2.4%
4991.6 76
 
2.4%
5008.7 47
 
1.5%
5017.5 95
 
3.0%
5023.5 19
 
0.6%
5076.2 140
 
4.5%
5099.1 670
21.5%
5176.3 1
 
< 0.1%
5191 487
15.6%
5195.8 337
10.8%
ValueCountFrequency (%)
5228.1 1169
37.5%
5195.8 337
 
10.8%
5191 487
15.6%
5176.3 1
 
< 0.1%
5099.1 670
21.5%
5076.2 140
 
4.5%
5023.5 19
 
0.6%
5017.5 95
 
3.0%
5008.7 47
 
1.5%
4991.6 76
 
2.4%

y
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size48.7 KiB
0
2743 
1
373 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3116
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2743
88.0%
1 373
 
12.0%

Length

2023-11-03T20:30:39.221929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-03T20:30:39.266138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2743
88.0%
1 373
 
12.0%

Most occurring characters

ValueCountFrequency (%)
0 2743
88.0%
1 373
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3116
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2743
88.0%
1 373
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3116
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2743
88.0%
1 373
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2743
88.0%
1 373
 
12.0%

Interactions

2023-11-03T20:30:36.717989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:33.848300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.389074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.809545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.241195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.623211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.974450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.331965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.780360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:33.957815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.446719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.871103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.294347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.668020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.022923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.409099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.842744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.058349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.495687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.954091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.340487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.717079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.067067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.456117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.894267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.134438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.545002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.006197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.388425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.762379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.110651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.502546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.938666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.184624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.591898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.049573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.435099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.803762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.152581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.543576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:37.085945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.232585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.643687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.101515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.485311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.847608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.194472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.586765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:37.128951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.284609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.697901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.150550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.530177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.890436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.236608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.628630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:37.173202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.341079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:34.753645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.194168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.577312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:35.933240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.277873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-11-03T20:30:36.671664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-11-03T20:30:39.305139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
agecampaignpreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedjobmaritaleducationdefaulthousingloancontactmonthday_of_weeky
age1.000-0.0230.023-0.015-0.0070.072-0.001-0.0100.2470.2630.1220.0000.0000.0000.0650.0970.0530.147
campaign-0.0231.000-0.0860.1770.1300.0270.1630.1530.0000.0160.0130.0000.0000.0000.0960.0460.0000.062
previous0.023-0.0861.000-0.436-0.250-0.087-0.471-0.4540.0390.0330.0000.0180.0000.0110.2360.1470.0130.266
emp.var.rate-0.0150.177-0.4361.0000.6870.2300.9360.9320.1260.0410.0570.0320.0500.0320.4380.6540.0400.335
cons.price.idx-0.0070.130-0.2500.6871.0000.2180.4920.4410.1290.0390.1020.0000.0890.0000.6590.6740.0530.340
cons.conf.idx0.0720.027-0.0870.2300.2181.0000.2310.1400.1080.0360.0780.0000.0470.0350.3810.6090.0500.397
euribor3m-0.0010.163-0.4710.9360.4920.2311.0000.9340.1340.0520.0580.0390.0610.0000.4430.6000.1560.411
nr.employed-0.0100.153-0.4540.9320.4410.1400.9341.0000.1250.0570.0670.0000.0530.0350.4490.5970.0370.416
job0.2470.0000.0390.1260.1290.1080.1340.1251.0000.1630.4120.0870.0640.0000.0730.1120.0000.108
marital0.2630.0160.0330.0410.0390.0360.0520.0570.1631.0000.0930.0000.0000.0000.0660.0300.0000.025
education0.1220.0130.0000.0570.1020.0780.0580.0670.4120.0931.0000.0000.0480.0000.1100.0950.0380.041
default0.0000.0000.0180.0320.0000.0000.0390.0000.0870.0000.0001.0000.0000.0000.0000.0000.0030.000
housing0.0000.0000.0000.0500.0890.0470.0610.0530.0640.0000.0480.0001.0000.0670.0660.0560.0000.000
loan0.0000.0000.0110.0320.0000.0350.0000.0350.0000.0000.0000.0000.0671.0000.0000.0000.0320.000
contact0.0650.0960.2360.4380.6590.3810.4430.4490.0730.0660.1100.0000.0660.0001.0000.5620.0450.145
month0.0970.0460.1470.6540.6740.6090.6000.5970.1120.0300.0950.0000.0560.0000.5621.0000.0700.265
day_of_week0.0530.0000.0130.0400.0530.0500.1560.0370.0000.0000.0380.0030.0000.0320.0450.0701.0000.000
y0.1470.0620.2660.3350.3400.3970.4110.4160.1080.0250.0410.0000.0000.0000.1450.2650.0001.000

Missing values

2023-11-03T20:30:37.241605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-03T20:30:37.358605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
030blue-collarmarriedbasic.9y010cellularmayfri20-1.892.893-46.21.3135099.10
139servicessinglehigh.school000telephonemayfri401.193.994-36.44.8555191.00
225servicesmarriedhigh.school010telephonejunwed101.494.465-41.84.9625228.10
447admin.marrieduniversity.degree010cellularnovmon10-0.193.200-42.04.1915195.80
532servicessingleuniversity.degree000cellularsepthu32-1.194.199-37.50.8844963.60
632admin.singleuniversity.degree010cellularsepmon40-1.194.199-37.50.8794963.60
831servicesdivorcedprofessional.course000cellularnovtue11-0.193.200-42.04.1535195.80
1136self-employedsinglebasic.4y000cellularjulthu101.493.918-42.74.9685228.10
1236admin.marriedhigh.school000telephonemaywed201.193.994-36.44.8595191.00
1347blue-collarmarriedbasic.4y010telephonejunthu201.494.465-41.84.9585228.10
agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy
410832admin.marrieduniversity.degree010telephonemaythu50-1.892.893-46.21.2665099.10
410963retiredmarriedhigh.school000cellularoctwed10-3.492.431-26.90.7405017.50
411130technicianmarrieduniversity.degree001cellularjunfri11-1.794.055-39.80.7484991.60
411231techniciansingleprofessional.course010cellularnovthu10-0.193.200-42.04.0765195.80
411331admin.singleuniversity.degree010cellularnovthu10-0.193.200-42.04.0765195.80
411430admin.marriedbasic.6y011cellularjulthu101.493.918-42.74.9585228.10
411539admin.marriedhigh.school010telephonejulfri101.493.918-42.74.9595228.10
411627studentsinglehigh.school000cellularmaymon21-1.892.893-46.21.3545099.10
411758admin.marriedhigh.school000cellularaugfri101.493.444-36.14.9665228.10
411834managementsinglehigh.school010cellularnovwed10-0.193.200-42.04.1205195.80

Duplicate rows

Most frequently occurring

agejobmaritaleducationdefaulthousingloancontactmonthday_of_weekcampaignpreviousemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedy# duplicates
028servicesmarriedhigh.school000cellularjultue101.493.918-42.74.9625228.102
128studentsinglebasic.9y010cellularmartue10-1.892.843-50.01.7995099.112
231admin.marrieduniversity.degree000cellularaugwed101.493.444-36.14.9645228.102
331admin.singleuniversity.degree010cellularnovthu10-0.193.200-42.04.0765195.802
432admin.marriedhigh.school000cellularmaythu10-1.892.893-46.21.3275099.102
532admin.singleuniversity.degree010cellularaugthu101.493.444-36.14.9645228.102
632admin.singleuniversity.degree010cellularjulthu21-1.794.215-40.30.8464991.602
732servicesmarriedhigh.school000telephonemaytue101.193.994-36.44.8575191.002
833admin.singlehigh.school010cellularaugtue101.493.444-36.14.9635228.102
933admin.singleuniversity.degree010cellularmaythu10-1.892.893-46.21.3275099.102